EP3095656B1 - Method for selecting an operating mode of a hybrid vehicle - Google Patents

Description

The invention relates to a method for selecting an operating mode of a hybrid vehicle, in particular a method in which from a given set of possible operating modes of a hybrid powertrain optimal for the current operating time with respect to predetermined target criteria operating mode is selected with a corresponding optimal power distribution in the drive train.

Constantly rising fuel costs as well as stricter statutory requirements regarding vehicle emissions are placing increasing demands on the efficiency of motor vehicles. A great potential for reducing fuel consumption is the hybridization of the drive train. In this case, the drive train is extended by at least one electrical machine and an associated electrical storage system.

Depending on the design of the hybridization, various other operating modes, such as electric driving, a boost operation, a brake recuperation, etc., can be represented in addition to a purely internal combustion engine operation. The various modes of operation of a hybrid powertrain must be sensibly used in vehicle operation in order to achieve the greatest possible reduction in fuel consumption. At the same time, other goals should also be taken into account, since the increase in fuel efficiency always takes place in the area of conflict between emission minimization, component protection and / or driving comfort. In order to realize an optimum operating strategy of the hybrid vehicle, it is therefore necessary to select an optimum operating mode with a corresponding optimum power distribution in the drive train from a predefined set of possible operating modes of the hybridized powertrain for the current operating time with regard to the predefined target criteria.

1. [1]: A. Wilde, Eine modulare Funktionsarchitektur für adaptives und vorausschauendes Energiemanagement in Hybridfahrzeugen, Dissertation, TU München, 2009 ; und
2. [2]: J. von Grundherr, Ableitung einer heuristischen Betriebsstrategie für ein Hybridfahrzeug aus einer Online-Optimierung, Dissertation, TU München, 2010 ; und
3. [3]: Onori et al., Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles, Proceedings of the ASME 2010, Dynamic Systems and Control Conference DSCC2010 September 12-15, 2010, Cambridge (MA), USA .

In order to select a respective optimum operating mode within the framework of an operating strategy, various techniques and methods are known from the prior art. For example, reference is made to the following publications from the prior art:

1. [1]: A. Wilde, A Modular Functional Architecture for Adaptive and Predictive Energy Management in Hybrid Vehicles, Dissertation, TU Munich, 2009 ; and
2. [2]: J. von Grundherr, derivation of a heuristic operating strategy for a hybrid vehicle from an online optimization, dissertation, TU Munich, 2010 ; and
3. [3]: Onori et al., Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicles, Proceedings of the ASME 2010, Dynamic Systems and Control Conference DSCC2010 September 12-15, 2010, Cambridge (MA), USA ,

According to the publication [1], page 19 et seq., The various techniques and methods for defining an operating strategy for a hybridized powertrain can be classified as follows:
According to so-called offline or static operating strategies, all use cases of the vehicle are already determined in advance and offline on the basis of fixed specifications according to which the energy balance in the vehicle is set. Such specifications or rules can be implemented, for example, with state machines or continuous controllers.

Furthermore, optimal controls are known which include a prediction horizon in the optimization problem (also called model-based predictive control (MPR)). Such approaches determine based on their input information, in a certain preview window (prediction horizon) in a given search space for the model under consideration an optimal trajectory of a state variable of the system, for example, the state of charge of the energy storage.

Furthermore, AI method-based operating strategies for controlling the energy systems in the hybrid vehicle are known. An attempt is made to present fuzzy knowledge using mathematical methods, such as neural networks, fuzzy logic, economic models or approaches to game theory, in such a way that computer systems can process the fuzzy knowledge.

Finally, methods based on so-called on-line optimizations are known, which understand the vehicle as a multi-dimensional optimization problem described by an objective function involving not only physical quantities such as consumption, but also other requirements such as noise emission or lifetime protection of components, etc. to include. Thus, for example, publication [2] proposes a total cost function by means of which, in addition to the target criterion "fuel efficiency", further target criteria such as, for example, drivability (ride comfort) are taken into account. In this case, partial cost functions correspond to the individual target criteria combined into a total cost function by means of a summation and then calculates the decision regarding the operating strategy from the total cost function.

The US 2015/0051775 A1 and the US 2011/0130901 A1 disclose methods for selecting a mode of operation of a hybrid vehicle in which, from a given set of possible modes of operation of a hybrid powertrain, an optimum mode with a corresponding optimal power distribution in the driveline is selected for the current operating time with respect to predetermined target criteria.

A disadvantage of the known approaches, however, is that the optimization approaches based on such a combined total cost function require high computational effort and often are not suitable for real-time operation with regard to the limits of the computing capacity of current control devices. Furthermore, these approaches have the disadvantage that the optimization methods can not usually be adapted to further or changed target criteria with little adaptation effort, since interactions between the individual target criteria exist and adding additional sub-cost functions usually requires complex changes to the analytical formula of the total cost function ,

It is thus an object of the invention to provide an improved method for selecting a mode of operation of a hybrid vehicle, with which disadvantages of conventional techniques can be avoided. The object of the invention is, in particular, to provide a method for selecting a mode of operation of a hybrid vehicle in which, from a predefined set of possible modes of operation of a hybrid powertrain, an optimum mode of operation for the current operating time with respect to predetermined target criteria can be selected and with which the risk of erroneous decisions is minimized and with the same amount of computing time can be saved. Furthermore, a method is to be provided which can be easily and intuitively extended to additional target criteria.

These objects are achieved by a method having the features of the main claim. Advantageous embodiments and applications of the invention are the subject matter of the dependent claims and are explained in more detail in the following description with partial reference to the figures.

According to general aspects of the invention, a method for selecting a mode of operation of a hybrid vehicle is proposed in which, in a multi-stage process, one of the set of possible modes of operation of the vehicle or of the drive train with regard to predetermined optimization criteria, optimum operating mode is selected together with an optimal power distribution in the drive train. The optimization criteria are also referred to below as the target criteria. In this case, the decision to determine an optimal power distribution in the drive train is decoupled from the decision or selection of an optimal operating mode, ie, first the optimum power distribution is determined determined for each possible operating mode and then selected on the basis of this calculation result, the optimum operating conditions with respect to the target criteria.

In accordance with these general aspects of the invention, there is thus provided a method of selecting the mode of operation of a hybrid vehicle in which a selected optimum operating mode with a corresponding optimum power distribution in the driveline is selected from a given set of possible modes of operation of a hybrid powertrain for the current operating time with respect to predetermined target criteria. For each of the predetermined target criteria, at least one evaluation variable is defined or determined in advance for the quantitative description of the respective target criterion. The term "advance" is understood to mean a determination of the evaluation variables that are "offline", ie. H. not in the driving operation of the vehicle takes place, but already before or at the programmable device of the vehicle control, which is designed to carry out the online method.

For online calculation of the optimal mode selection, the method further comprises the following steps:
For each mode of operation of a first choice of possible modes, the possible power distributions in the powertrain and the values of the evaluation variable quantitatively describing the target criteria are determined, the values of the evaluation variables being calculated for each of the particular power distributions of the respective mode, at least if their values are dependent on the power distribution , Each operating point of a possible power distribution of an operating mode is thus assigned in each case a value of each of the evaluation variables. The target criterion of maximizing energy efficiency can be described quantitatively by, for example, a rating variable indicating the efficiency of the electric traction machine. The efficiency is then determined in each operating mode for each of the specific power distributions.

Then, for each mode of operation of the first selection of modes, an optimal power distribution in the powertrain is determined by means of the determined values of the evaluation variables.

Subsequently, an optimum operating mode is selected by means of those values of the evaluation variables which they have for the operating modes of the first selection in each case at the operating point of the determined optimum power distribution. As power distribution to be specified for the selected optimal mode of operation, preferably, the corresponding predetermined optimal power distribution for that mode is set.

In other words, it is thus determined for each operating mode independently of the other which would be the currently optimal power distribution in the drive train. One advantage of this is that it avoids cross-influences from other modes in determining the optimal power distribution for a mode of operation. Only in a subsequent step, the individual operating modes are then compared with each other by means of the evaluation variables, for which purpose only those values of the valuation variables are used which have these in the operating points of the previously determined optimal power distributions.

The term operating mode of a hybrid powertrain is understood in this document to mean a combination of a specific powertrain configuration and the form of the energy flows in the components involved. The possible modes of operation of the powertrain may include at least two of the following operating modes known per se: a purely internal combustion engine driving; a purely electric driving with the internal combustion engine switched off; an electric driving with internal combustion engine in the idling mode; a boost operation in which the total drive power is above the power that currently can be provided solely by the internal combustion engine, and wherein the additional power is provided by the electric traction machine powered by the electric traction energy storage; a sailing operation, d. H. a rolling operation in which the internal combustion engine and / or the regenerative electric machine are not dragged by the kinetic energy of the vehicle; a brake recuperation; an electric starting; a load point boost; a load point reduction; and a so-called GenSet operation, in which the output from the engine power is converted by the electric machine into electrical power to supply the ancillaries and / or to charge the electrical energy storage without a moment is put on the drive wheels.

A power distribution indicates the distribution of the total power of the drive train to the individual components of the drive unit in the hybridized powertrain with regard to a driver's current requested performance (eg in the form of a positive or negative driver request torque). According to an emphasized embodiment of the invention, the hybrid vehicle is a parallel hybrid vehicle with an internal combustion engine and an electric traction engine, which are operable independently of each other, but on a common shaft so that they are always moved in the coupled case at the same speed and add their moments. In this case, the power distribution in the powertrain corresponds to the distribution of the driver's desired torque to the torque (torque) of the internal combustion engine and the torque (moment) of the electric machine. For a given driver's desired torque, the possible power distributions in the drive train for each operating mode can thus be indicated by the torques that can be generated by the electric traction machine in this operating mode, which are limited by the performance of the electric machine. Accordingly, the optimal power distribution per mode for a parallel hybrid vehicle may be indicated by an optimal torque of the electric traction machine with respect to the predetermined target criteria. It is emphasized, however, that the method is not limited to parallel hybrid vehicles.

The optimal selection of an operating mode is understood as the selection of that operating mode which offers the best possible compromise with regard to the several predetermined target criteria.

In addition to the above-mentioned maximization of energy efficiency as a target criterion for the selection of the optimal operating mode, further target criteria can be specified, such as the conditioning of the energy storage, a fuel consumption minimization and / or the highest possible driving comfort. It has already been mentioned above that for each of the target criteria at least one evaluation variable is defined, which can quantitatively describe the respective target criterion. The target criterion of driving comfort can be described quantitatively, for example, with a rating variable that indicates a change in the current torque, in particular the occurrence of a torque jump. The higher the torque jumps, the lower the ride comfort for the driver. For the quantitative description of the fuel consumption minimization, for example, an evaluation variable can be used which indicates the fuel mass flow permeated in the cylinders of an internal combustion engine.

According to a preferred embodiment of the invention, the following steps are performed to determine the optimum power distributions, which are respectively determined for each operating mode of the first selection of possible operating modes, the steps being carried out for each operating mode of the first selection:
Determining an optimum value of each evaluation variable in the respective operating mode, which is determined on the basis of the previously determined values of the respective evaluation variable in the respective operating mode. This optimum value is hereinafter referred to as "first optimum value" to distinguish this optimum value from a subsequent "second optimum value", which is determined in the context of the subsequent selection of the optimum mode.

According to a further step of this preferred embodiment, then for determining the optimum power distribution of each mode for each rating variable, a weighted first deviation of the values of the respective rating variable from the determined first optimum value of the respective rating variable is determined. As mentioned above, these weighted first deviations for the rating variables are also determined in each mode. For each operating point of a possible power distribution of an operating mode, this weighted first deviation of each of the evaluation variables is thus calculated. To calculate the weighted first deviations, it is preferable to specify corresponding weighting factors which evaluate the deviations of the individual weighting variables from their first optimum value and scale to a common comparison base.

In accordance with a further step of this preferred embodiment, a determination of the optimal power distribution of the respective operating mode is subsequently carried out using a predetermined first decision rule which defines an optimum power distribution as a function of the determined weighted first deviations of the evaluation variables.

If the target criterion of a valuation variable whose first optimum value is determined is an extremization target, then the first optimal value is set as an extreme value of the determined values of the respective evaluation variable in the respective operating mode.

If the destination of the extremity is an aim of maximization, for example maximizing the energy efficiency of the electric traction machine or maximizing ride comfort, the maximum value of the values of the evaluation variables that result for the possible power distributions of a mode is set as the respective first optimum value.

However, the target criterion of a rating variable is an approximation target, for example, if the target criterion is to ensure that the traction energy storage is predetermined Complies with state of charge limits, the first optimum value can be specified as an independent of the specific values of the evaluation variable in the respective operating mode setpoint, z. B. in the form of a desired value for the current target power of the electric traction energy storage, which depends on current vehicle information, eg. B. the current state of charge is calculated.

An advantage of this embodiment is that the various target criteria do not have to be summed up in the form of individual partial cost functions to form a total cost function. Instead, each target criterion is first evaluated on its own and independently of the other target criteria with regard to the different operating modes and possible power distributions, and then transferred by means of the weighted deviations to a common evaluation scheme. This approach offers the advantage that the process can easily be extended by further desired target criteria if required. In this case, it is only necessary to define a suitable further evaluation variable for the new target criterion, which quantitatively describes the target criterion, and to carry out the aforementioned steps analogously in addition to the further evaluation variable. An adaptation of a complex mathematical formula, as would be necessary for a total cost function, is not required.

According to another preferred embodiment, the following steps are performed to select an optimal mode of operation from the first selection of possible modes of operation:
For each valuation variable, a second optimal value of the valuation variable, which is valid for all operating modes of the first selection, is determined on the basis of its specific values. In contrast to the first optimum value, which is determined as the "local" optimum value for each evaluation variable in each operating mode, the second optimum value is determined as a "global" optimum value valid for all operating modes, so that a second optimum value is determined for each evaluation variable, independently from the operating mode.

According to this further preferred embodiment, a weighted second deviation determined for each evaluation variable in each mode of the first selection and each having a weighted deviation of the second optimum value of the respective evaluation variable from the value of the evaluation variable at the operating point of the first variable certain optimum power distribution for the respective operating mode indicates. In other words, for each mode, the first Selection determines a weighted difference in magnitude that indicates a deviation of the value of the evaluation variable in the optimal performance distribution from the mode-independent second optimal value of the evaluation variable.

Subsequently, the selection of the optimum mode is performed using a predetermined second decision rule which evaluates the modes of the first selection as a function of the weighted second deviations of the evaluation variables and determines an optimal mode of operation.

If the target described by the evaluation variable is an extremity target, then the second optimum value is preferably set as an extreme value of the values of the respective evaluation variable at the operating point of the previously determined optimal power distribution in all modes of the first selection. The second optimum value of a particular evaluation variable thus corresponds to the maximization value of the values of these evaluation variables in the different operating modes at the operating point of the optimal power distribution. If the target criterion is a minimization target, then the minimum of these values is set as the second optimum value. However, if the target criterion described by the evaluation variable is an approximation target, the second optimum value is given as a set point independent of the determined values of the respective evaluation variable, which in turn can be calculated in accordance with current vehicle information.

It has already been mentioned above that in order to determine the weighted first deviation or the determination of the weighted second deviation, at least one weighting factor or weighting factor can be provided for each weighting variable which determines the absolute difference of the values of the weighting variables from the first and second optimum values of the respective Valuation variable on a dimensionless basis or common cost base for all valuation variables, d. H. a base with a common unit, scaled.

By means of the weighting factors, assessment variables that quantitatively describe a wide variety of target criteria, such as the maximization of the energy efficiency of the electric traction engine, fuel consumption minimization, emission minimization, etc., can be transferred to a common and therefore comparable basis and evaluated at the same time. A rating in this sense means that a deviation of, for example, x% of a first variable that, for example, evaluates energy efficiency, from its Optimal value may be rated more critically than a 10% deviation of a second evaluation variable describing an emission target from its optimum value. This different weighting or evaluation of the evaluation variables can thus be carried out in a simple manner on the basis of the determination of the weighting factors.

It has already been mentioned above that the determination of the optimum power distribution of each operating mode is carried out using a first decision rule which is supplied as input data with the determined weighted first deviations of the evaluation variables from which an optimal element is selected in accordance with the decision rules. Similarly, a second decision rule is used to select the optimal mode, which is fed as input data, the weighted second deviations of the evaluation variables.

Such selection of an optimal element from a plurality of evaluation variable-valued variants using decision rules is known per se from the field of game theory or the field of multi-criteria decision problems. As decision rules, the various types of decision rules known per se in these fields can be used, such as the minimax principle or the so-called lexicographical order, etc. It should be noted that the known decision rules each have different strengths and weaknesses, so that the Using a particular decision rule always brings certain advantages and disadvantages.

It has been found in the context of the invention that it is particularly advantageous for the present inventive method for selecting an optimal operating mode if the second decision rule is such that the weighted second deviations of the evaluation variables are respectively summed up for one operating mode and the operating mode is set as the optimum operating mode whose sum value of the summed second deviations is minimal. According to this variant, the operating mode is thus selected on the basis of the sum values as the optimum operating mode, which has the lowest overall damage sum in the form of the summed second deviations.

On the other hand, it is particularly advantageous if the first decision rule, which is used to evaluate the first deviations, ie the deviations from the local first optimum values, is a minimax decision rule. Here, according to the first decision rule at each operating point of the possible power distributions of a mode in each case the largest first deviation is determined from the values of the weighted first deviations of the evaluation variables and the operating point which is the smallest of the greatest first deviations determined in this way is defined as the operating point of the optimum power distribution. According to this variant, to determine the optimal power distribution according to the minimax principle, the "worst case" is minimized, which according to findings of the inventors in the determination of the optimal power distribution allows a better compromise of the target criteria than a simple addition.

A further particularly advantageous embodiment of the invention provides that the first selection of the operating modes on the basis of which the aforementioned determination of the optimal power distribution per operating mode and the subsequent determination of the optimum operating mode is already possible by operating modes of the hybrid vehicle which are possible in principle Current vehicle movement state are not possible and / or are excluded due to other restriction rules, is adjusted. This limitation of the operating modes allows a significant reduction of the computational effort in the subsequent determination of the optimal power distribution and the optimal mode.

According to this variant, the determination of the first selection of the operating modes thus comprises the following steps: specification of possible vehicle movement states of the hybrid vehicle, specification of possible operating modes of the drive train and specification of an assignment which indicates which of the possible operating modes are permissible in which vehicle movement state; Determining a current vehicle motion condition; and restricting the modes of operation of the vehicle to those associated with the determined current vehicle motion state.

The predetermined possible vehicle movement states may include, for example, a vehicle standstill, a braking operation and a driving operation (without braking), hereinafter also referred to as "driving". Thus, for example, a vehicle movement state "driving" the modes of combustion engine driving, electric driving, load point increase and load point reduction can be assigned. The vehicle movement condition "braking" may be assigned to the modes of conventional braking, brake regeneration and sailing. The vehicle movement state "standstill" may be assigned the operating modes idle and the GenSet.

This reduced as a function of the vehicle motion state amount of modes can advantageously be further reduced by at least one restriction rule is specified, which determines depending on at least one operating parameter and preferably independent of a current torque request and the current vehicle motion state, which modes are currently available. In accordance with this at least one restriction rule, a further restriction of the operating modes of the vehicle can then be made to those currently available. For example, according to the at least one restriction rule, an availability of components in the drive train can be tested, in particular an electrical energy store and / or an electric traction machine. For example, the restriction rule can specify that all operating modes that require the use of the electrical machine are currently not permitted if the electrical energy store exceeds predetermined temperature limits and / or is outside of predetermined charge state limits. In this way, those operating modes that are not available in the current operating state of the vehicle can thus be excluded even before the optimal power distribution for each operating mode is calculated.

According to a further aspect of the invention, in determining the optimum power distribution per operating mode, predetermined values of the power distributions of the operating modes which are to be excluded as possible operating points can be determined on the basis of predefined exclusion criteria. The exclusion criteria are preferably defined so that any potential distribution of benefits that is not excluded is generally an acceptable selection candidate. Thus, the optimal mode of operation and / or optimum power distribution are sought only on the basis of comparatively "good" power distributions and the risk of a wrong decision is reduced.

The invention further relates to a hybrid vehicle, in particular a utility vehicle, comprising a control device for selecting an operating mode of the hybrid vehicle that is configured to perform the method as disclosed herein. In order to avoid repetition, features disclosed purely in accordance with the method should also be disclosed as device-specific features of the control device and should be able to be claimed.

Figur 1

ein schematisches Ablaufdiagramm eines Verfahrens zur Auswahl einer Betriebsart eines Hybridfahrzeugs gemäß einer Ausführungsform der Erfindung;

Figur 2

ein alternatives Ablaufdiagramm zur Illustration des erfindungsgemäßen Verfahrens gemäß einer Ausführungsform;

Figur 3

ein Ablaufdiagramm der Schritte zur Bestimmung eines optimalen E-Moments für jede Betriebsart gemäß einer Ausführungsform der Erfindung;

Figur 4

ein Ablaufdiagramm zur Illustration der Bestimmung der optimalen Betriebsart auf Basis der bestimmten optimalen Leistungsverteilungen gemäß einer Ausführungsform der Erfindung;

Figur 5

eine alternative Darstellung zur Illustration der Bestimmung der optimalen Leistungsverteilungen und der optimalen Betriebsart gemäß einer Ausführungsform der Erfindung;

Figur 6

eine beispielhafte Illustration der Berechnung einer optimalen Leistungsverteilung für eine Betriebsart;

Figur 7

eine beispielhafte Illustration der Bestimmung der optimalen Betriebsart auf Basis der bestimmten optimalen Leistungsverteilungen; und

Figur 8

eine Zuordnung, die angibt, welche der möglichen Betriebsarten in welchem Fahrzeugbewegungszustand zulässig sind.

The preferred embodiments and features of the invention described above can be combined with one another as desired. Further details and advantages of the invention will be described below with reference to the accompanying drawings. Show it:

FIG. 1

a schematic flow diagram of a method for selecting an operating mode of a hybrid vehicle according to an embodiment of the invention;

FIG. 2

an alternative flowchart for illustrating the method according to the invention according to an embodiment;

FIG. 3

a flowchart of the steps for determining an optimal E-moment for each mode according to an embodiment of the invention;

FIG. 4

a flow chart illustrating the determination of the optimal mode based on the determined optimal power distributions according to an embodiment of the invention;

FIG. 5

an alternative representation for illustrating the determination of the optimal power distributions and the optimal mode according to an embodiment of the invention;

FIG. 6

an exemplary illustration of the calculation of an optimal power distribution for a mode;

FIG. 7

an exemplary illustration of the determination of the optimal mode based on the determined optimal power distributions; and

FIG. 8

an assignment that indicates which of the possible modes are allowed in which vehicle movement state.

FIG. 1 shows an exemplary flowchart for illustrating the steps of a method for selecting a mode of operation of a hybrid vehicle according to a preferred embodiment. According to the present embodiment, the hybrid vehicle is designed as a parallel hybrid vehicle, in particular as such a commercial vehicle. The per se known hybrid powertrain comprises an internal combustion engine and an electric traction machine, wherein the internal combustion engine and the electric traction machine, hereinafter also referred to as electric machine or electric motor, speed-coupled via a common shaft, but are independently controllable.

With the hybrid powertrain different known per se modes can be performed, for example, a purely internal combustion engine driving, a purely electric driving, a boost operation, a sailing operation, a Bremsrekuperation, an electric Starting, a load point increase, a load point reduction, a so-called GenSet operation, etc.

The various modes of operation should be used meaningfully in vehicle operation, in order to achieve the greatest possible reduction in fuel consumption, with other predetermined objectives to be considered, such as maximizing the energy efficiency of the electric traction engine, the emission minimization and / or ride comfort. To realize an optimal operating strategy of the hybrid vehicle, it is therefore necessary to select from a given set of possible operating modes of the hybridized powertrain an optimal operating mode with respect to the predetermined target criteria with a corresponding optimal power distribution in the drive train, d. H. to select the operating mode in each operating time, which offers the best possible compromise in terms of the target criteria.

In step S1, at least one suitable evaluation variable for each of the target criteria is initially defined offline, ie before the actual online optimization process is carried out, by means of which the respective target criterion can be described quantitatively. This is exemplified in more detail below with reference to the FIGS. 6 and 7 explained.

During operation of the vehicle and in the context of the actual online optimization process, steps S2 to S5 are then carried out continuously, ie. That is, the steps S2 to S5 are performed again every few milliseconds so as to continuously select and set the optimum mode for the current vehicle state depending on current vehicle values.

According to steps S2 to S5, in a multi-stage process, out of the set of all possible operating modes possible with the system, an optimum operating mode with respect to the optimization criteria is selected together with an optimal power distribution.

In step S2, this is done first of all, a reduction of the modes to a currently valid amount, which in the Figures 2 and 8th is explained in more detail.

At a start time of the optimization or of the respective calculation cycle, the multistage selection method initially presents all operating modes BA1 to BAn that can be realized with the hybridized drive train for evaluation.

FIG. 8 lists the possible vehicle movement states 11 of the hybrid vehicle: The predefined possible vehicle movement states are, for example, vehicle standstill, brake operation and driving operation (without brakes). As is known, not every one of the operating modes can be realized in each of the possible vehicle movement states 11 of the vehicle. The assignment 12, which indicates which of the possible operating modes 12 are possible in each case in a specific vehicle movement state 11, was determined in advance and z. B. in the form of a table in the vehicle, which is accessed in step S2.

For example, it can be seen from the assignment 12 that, for example, a vehicle movement state "driving" is assigned the operating modes of combustion engine driving, electric driving, load point raising and load point lowering. The vehicle movement state "brakes" are assigned to the modes conventional braking, brake recovery and sailing. The vehicle movement state "standstill" is assigned the operating modes idle and the GenSet.

In the FIG. 8 shown operating modes per vehicle movement state are only examples and can be extended by other possible modes, depending on the design of the hybrid vehicle.

In step S21 of FIG. 2 First, the current vehicle movement state is determined and then the restriction of the operating modes of the vehicle to those made to the particular current vehicle movement state according to the in FIG. 8 assigned assignment 12 are assigned. Thus, if the vehicle is currently in the vehicle movement state "driving", in the following step S22 only those operating modes are again supplied which are assigned as possible operating modes via the vehicle movement state "driving". The reduced amount of modes 1b is then further processed in step S22.

In step S22, restrictions on individual operating modes are evaluated on the basis of defined rules. Restrictions go z. B. from the availability of components. Such restrictions may include, for example, thermal restrictions that check whether the electric machine or traction energy storage of the electric machine has become too hot, so that those modes that require the electric machine are not available at the current time and consequently in this Case be excluded from the list of possible operating modes. Such restriction rules can also check if the machine is available for other reasons or not. However, these restrictions are not torque-related, but such criteria are only in the subsequent steps (eg S45, S55), z. For example, within the exclusion criteria. The number of possible operating modes 1b can therefore optionally be further restricted in step S22 on the basis of predetermined restriction rules, so that finally a first selection of possible operating modes 1c is supplied to the subsequent two-stage selection process of steps S3 to S5. For example only, it shall be assumed that in the current driving state and calculation time of the method, the possible modes of operation of the first selection 1c have been reduced to only three operating modes, which are referred to below as BA1, BA2 and BA3.

In step S3, the in FIG. 2 is not shown, first possible power distributions in the drive train are determined for each of the remaining after the reduction of the operating modes 1c. In the present exemplary embodiment of a parallel hybrid vehicle, the possible power distribution is given by specifying the torques that can be realized with the electric machine in the current operating state. When calculating the possible moments of the electric motor, the efficiency of the electric motor is taken into account. From each possible moment of the electric motor, the remaining torque to be applied by the internal combustion engine then results as the difference between the currently requested driver's desired torque minus the torque of the electric motor, so that the power distribution is clearly defined. As a result of step S3, a range of possible values for the E machine torques thus results for each operating mode. The possible values for the e-machine torques are calculated in discrete steps, eg. B. in steps of 10 Nm.

In step S4 then the calculation of the optimal E-machine torque per mode takes place. The substeps of step S4 are in the Figures 3 . 5 and 6 each illustrated in different representations.

In the upper diagram of the FIG. 6 For example, the values of the abscissa indicate the possible e-moments determined in step S3 of one of the operating modes of the first selection 1c, here by way of example for the operating mode BA2. In the present case, the operating mode BA2 represents the operating mode of a load point reduction, in which the power required to satisfy the positive driver request torque is provided both by the internal combustion engine and by the electric motor. The load point reduction is used for the operating point optimization of the internal combustion engine or for the targeted discharge of the energy store. For this mode In the current operating state, possible E-moments 2 in the range of 0-600 Nm were determined in discrete steps of 10 Nm (see step S3).

In steps S41 and S42, the values of the evaluation variables for each operating mode are then calculated and then the first optimum value is determined for each evaluation variable in the respective operating mode.

The curves 5 and 6 drawn in the diagram and the horizontal line 7 indicate the course of the values of three evaluation variables calculated in step S41 in dependence on the specific possible moments 2 of the electric motor or the power distribution.

According to the present example, the curve 5 indicates the course of a rating variable "efficiency of the electric traction machine". The unit of this evaluation variable is dimensionless. This rating variable is used to quantitatively describe the target criterion of maximizing the energy efficiency of the electric traction machine.

The target criterion of maximizing the energy efficiency of the electric traction machine is an maximization goal, i. h., energy efficiency should be maximized. Consequently, in step S42, as the first optimum value 5a of the evaluation variable "efficiency of the electric traction machine", the maximum value of the curve 5 is determined.

In the present case, the course 5 of this evaluation variable, depending on the moment 2a of the electric motor, has a maximum 5a at an e-machine torque value of z. B. 100 Nm. This means that the maximum efficiency is achieved with an E-machine torque of 100 Nm, d. H. when the electric machine sets 100 Nm in response to a positive current driver request torque and the remaining difference to the driver's desired torque is applied by the internal combustion engine. It can be seen from the course 5 that the efficiency drops again at a larger E-moment than 100 Nm. This is z. Example is that the power loss increases quadratically towards higher torques. The calculation of the course 5 takes place by means of a stored efficiency map 4a of the electric traction machine.

The marked with the reference numeral 6 line in the upper diagram of FIG. 6 represents a curve of the battery power and indicates the charging or discharging power of the energy storage, in which the efficiency of the traction energy storage is received. With higher e-moment, which is to be applied by the electric motor, the discharge power increases, which is represented by the slope of the curve 6. In this curve, no maximum or minimum of the curve 6 is searched, but the associated target criterion is a Approximierungsziel regarding the optimal state of charge of the energy storage, which should be in a predetermined range. If the traction energy store is fully charged, it is favorable to discharge it in order subsequently to again have sufficient storage capacity for receiving recuperation energy. If the traction energy storage heavily discharged, it is convenient to load this again. Consequently, the first optimum value for the course of the curve 6 is given as a desired value 6a as a function of current vehicle data, in particular as a function of the current state of charge of the traction energy store, which is achieved here by a value of the E torque at 440 Nm.

The course of the curve 7 is the course of a further evaluation variable for the evaluation of another goal. This rating variable is an example of a rating variable that is not dependent on the current e-moment. The course of such a rating variable shows up as a horizontal straight line above the e-moment and only gains significance in the subsequent step of comparing the different operating modes with one another. For this evaluation variable, a minimum is identified as the optimum value. Due to the constant course of the curve 7, this minimum 7a is not associated with a specific torque.

The first optimal values 5a, 6a, and 7a are determined not only for the mode BA2 but for all modes of the first selection.

The represented course 5, 6, 7 of the values of the evaluation variables is recalculated each time through the steps S2 to S5. The individual calculation steps are here information such as current measurement data, vehicle and component parameters and maps available that are processed by a vehicle controller 4, which is set up to carry out the method, and in the target sizes, exclusion criteria (KO criteria), weighting factors, to be used vehicle parameters, etc. are deposited, which is indicated by the block 4 only by way of example and highly schematic.

The calculation of the gradients 5, 6, 7 of the evaluation variables takes place as a function of current measured data, vehicle and component parameters as well as characteristic maps and is particularly dependent on the specific drive train configuration of the hybrid vehicle.

The curves 5, 6, 7 of the evaluation variables thus determined are only valid for one calculation cycle of the steps S2 to S5 of the online calculation, ie. That is, in a subsequent calculation cycle S2 to S5, which is executed a few milliseconds later or in another driving situation, other courses and other optimum values 5a, 6a, 7a may result, since the vehicle parameters and measurement data are constantly changing.

In step S43, for each evaluation variable of each mode, the weighted first deviation of the values of the respective evaluation variable from the determined first optimum value of the respective evaluation variable is determined. This is in the lower diagram of the FIG. 6 shown. To determine the weighted deviation, the difference values from the original curve 5, 6, 7 with the respective optimum value 5a, 6a or 7a are respectively formed for each curve and additionally scaled by a weighting factor (weighting factor).

Since the optimum value 5a is associated with an E-torque of just over 100 Nm, the course of the weighted first deviation 5c has a zero point in this torque value of the electric machine. The same applies to the weighted first course 6c of the battery power, which has a zero at the nominal value of approximately 440 Nm. The weighted curve 7c is difficult to see in the diagram, since it lies directly on the abscissa axis, which results from the constant curve 7 in the upper diagram. In the present case, this evaluation variable thus virtually disappears from the current evaluation because there is no optimum value.

By scaling with the weighting factor, the determined first deviations can be translated on the one hand to a dimensionless unit or to a common unit, e.g. a monetary unit of a damage function, because the valuation variables are usually given in different units. On the other hand, the weighting makes it possible to calibrate the first deviations of the evaluation variables among one another in order to prioritize the evaluation variables with one another, since as a rule the target criteria assigned to the evaluation variables should be weighted or prioritized to different degrees.

In FIG. 5 is further shown that optional exclusion criteria (in FIG. 5 can be specified as "KO criteria"), which can only allow certain e-machine torques. Thus, for example, in step S45 based on a predetermined exclusion criterion, only those E machine torques are permitted which have a minimum efficiency, for example E-moments in which the current efficiency the electric traction machine equals at least 80 percent. With these exclusion criteria, certain e-moments 9 of the possible power distribution per operating mode are thus excluded, which is indicated by the hatched areas in the lower diagram of the FIG. 6 is shown. In these areas, the values of the evaluation variable or the weighted first deviation are therefore not taken into account in the subsequent determination of the optimum operating mode.

An advantage of using such exclusion criteria is that they can be determined so that each of the E-moments not excluded thereby already represents an acceptable selection candidate, so that subsequently the optimum of the power distribution is sought only within acceptable power distributions or E-moments , This can reduce the risk of a wrong decision. Furthermore, weaknesses of the subsequently applied decision rules can thereby be compensated, since at least this ensures that an acceptable e-moment is always taken as the basis for the selection of the optimum mode of operation.

In step S44, an optimal E-machine torque 2a is subsequently determined using a predetermined decision rule.

For example, a simple addition of all curves 5c, 6c, and 7c and selection of that operating point would be possible, ie. H. that E engine torque having the lowest value of the added first weighted deviations 5c, 6c, and 7c of all the evaluation variables per operating mode. This represents the point of least total damage.

A disadvantage of this comparatively simple decision rule, however, is that the optimum can be below the optimum of a sub-goal. This is disadvantageous if one has found a very optimal solution with regard to a target criterion, but with respect to all other target criteria, however, a rather comparatively poor solution.

A preferable solution is therefore a good compromise between all the target criteria. For this purpose, a decision rule based on the minimax principle is better suited. In the lower diagram of the FIG. 6 Such a minimax decision rule has been applied, which is represented by the course of the dotted curve 10. The curve 10 indicates the worst case in which the greatest first deviation is determined in each case from the values of the weighted first deviations 5c, 6c, 7c at each operating point of the possible moments. The optimal power distribution then becomes the E-moment 2 a, to which the smallest 10 a of the determined largest first deviations 10 is assigned. This is the case in point 2a, since the dashed curve has its minimum 10a at the operating point 2a. It should be pointed out again that in FIG. 6 in each case only the determination of the optimal E moment for an operating mode, namely the operating mode "BA2", is shown. In the context of step S4, however, the values of the evaluation variables with the associated optimum values and the weighted deviations from the first optimum values are calculated for all operating modes of the first selection 1c, and then the optimum power distribution for the respective operating mode is determined.

The upper right diagram again shows the result of step S44 for operating mode BA2. From the possible values 2 of the E machine torques in the operating mode BA2, the point 2a is determined as the optimum E machine torque. At this optimal power distribution operating point 2a, the values 5b, 6b, and 7b are assigned as corresponding values of the evaluation variables, which are subsequently supplied to the step S5

In step S5, the determination of the optimum operating mode then takes place based on the results of step S4. For this purpose, the steps S51 to S54 or S55 are performed, which in turn in the FIGS. 4 . 5 and 7 illustrated in different representations.

In step S51, first, the optimum E-moments for each mode and the corresponding values of the evaluation variables 5b, 6b, 7b determined in step S44 are provided as input data, which is shown in the upper diagram of FIG FIG. 7 is exemplified for the three operating modes BA1, BA2 and BA3.

In the middle again is the operating mode BA2, which is already in FIG. 6 with its optimum E-moment 2a in the amount of, for example, 220 Nm and in each case the associated values 5b, 6b, 7b of the evaluation variable in this operating point 2a of the optimum power distribution.

To the left is the operating mode "BA1", which represents a mode of load increase. When boosting the load, the engine provides both the power to fully meet the positive driver torque demand and to simultaneously charge traction energy storage.

The operating mode BA3 represents a mode of purely electric driving.

It is a particular aspect of the present embodiment that comparable steps for determining the optimum operating mode are now carried out in a manner analogous to the steps for determining the optimum power distribution per operating mode, which works well in FIG. 5 is recognizable, since steps S52 to S55 correspond in principle to steps S42 to S45. The special feature when performing steps S52 to S55 is that the calculation steps are now run through with the optimal power distributions 2a of the operating modes and the associated values 5b, 6b, 7b of the evaluation variables.

In step S52, first the optimal values 5d, 6d, 7d of the evaluation variable are determined again. Whether the respective optimum value is a maximum value, a minimum value or a desired value, in turn, depends on the respective target criterion.

In order to maximize the energy efficiency of the electric traction machine again the maximum value is searched. But this time across all modes of operation, so that in each case the value 5b is determined as the maximum value 5d, which has the largest value in the operating point 2a of the optimal E-moment. In the present case this is the point 5b of the operating mode BA2, since the value 5b of the operating mode BA3 and the operating mode BA 1 is smaller.

For the battery power as evaluation variable, in turn, a desired value 6d is specified, which is set as the optimum value.

For the evaluation variable configuration change 7 the minimum value is searched. As the minimum value 7d, the value 7b of the height zero of the mode 3 is set as the optimum value. The optimum value 5d, 6d, 7d thus determined for each evaluation variable, referred to below as the second optimum value, applies to all operating modes BA1, BA2 and BA3 and is used to determine the weighted second deviation which is determined for each evaluation variable in each mode of the first selection in FIG Step S53 is determined.

The weighted second deviation 5e, 6e, 7e respectively represents a weighted deviation of the second optimum value 5d, 6d, 7d of the respective evaluation variable from the values of the respective evaluation variables 5b, 6b, etc. at the operating point 2a of the determined optimum power distribution for the respective mode.

Thus, for example, for the operating mode BA2, a value of 0 for the weighted second deviation 5e results for the operating variable "efficiency of the electric traction machine" since the second optimum value 5b is identical to the value of the evaluation variable for this operating mode. On the other hand, for the operating mode BA3 a positive difference in the absolute value of the weighted results second deviation 5e. Conversely, for example, the weighted second deviation 7e of the operating mode BA3 results in a value of 0, while the weighted second deviation 7e results in a positive absolute value value for the operating mode BA2.

Subsequently, in step S54, the determination of the optimum operating mode is carried out using a second decision rule according to which the operating modes BA1, BA2 and BA3 are respectively evaluated as a function of the weighted second deviations 5e, 6e and 7e of the evaluation variables and from this an optimum operating mode is determined ,

In the present case, the decision rule used is not the previously applied minimax decision rule. Rather, according to the second decision rule, the weighted second deviations 5e, 6e, 7e of the evaluation variables are respectively summed up into a mode of operation, resulting in the points Σ1, Σ2 and Σ3. Subsequently, the smallest of these sum values is determined, which in the present case corresponds to the value Σ2. Then, that mode is set as the optimum mode 3 having the smallest of these sum values. Thus, in the present case, the operating mode BA2 is set as the optimum operating mode. The optimum operating mode BA2, which corresponds to a load point reduction, is thus subsequently set by a control device as the current operating mode, wherein the previously determined in this mode optimal power distribution in the form of the optimal e-machine torque in the amount of just over 200 Nm specified as e-machine torque becomes.

Subsequently, a new calculation of an optimal mode 3 in the sequence of steps S2 to S5.

Although the invention has been described with reference to particular embodiments, it will be apparent to those skilled in the art that various changes may be made and equivalents may be substituted for without departing from the scope of the invention. In addition, many modifications can be made without leaving the associated area. Accordingly, the invention should not be limited to the disclosed embodiments, but should include all embodiments which fall within the scope of the appended claims. In particular, the invention also claims protection of the subject matter and the features of the subclaims independently of the claims referred to.

LIST OF REFERENCE NUMBERS

1a

Total possible operating modes

1b

Set of possible operating modes for a given vehicle movement state

1c

First choice of possible operating modes

2

Possible E machine torques for one operating mode

2a

Optimal E-machine torque for one operating mode

3

Optimal mode of operation

4

Vehicle control.

4a

Efficiency maps

5

Possible values of the evaluation variable Efficiency of the electric traction machine

5a

First optimum value of the variable efficiency

5b

Value of the variable efficiency at the operating point of the optimum e-machine torque

5c

Course of the weighted first deviation of the evaluation variable efficiency

5d

Second optimum value of the variable efficiency

5e

Second weighted deviation of the evaluation variable efficiency

6

Possible values of the evaluation variable battery power

6a

First optimum value of the battery power variable

6b

Value of the battery power variable at the operating point of the optimum e-machine torque

6c

Course of the weighted first deviation of the evaluation variable battery power

6d

Second optimum value of the variable battery power

6e

Second weighted deviation of the evaluation variable battery power

7

Possible values of the third evaluation variable

7a

First optimum value of the third evaluation variable

7b

Value of the third evaluation variable at the operating point of the optimum e-machine torque

7c

Course of the weighted first deviation of the third valuation variable

7d

Second optimal value of the third variable

7e

Second weighted deviation of the third weighting variable

9

Excluded e-moments according to exclusion criteria

10

Minimax curve

10a

Minimum of the minimax curve

11

Vehicle motion states

12

Assignment of possible operating modes to specific vehicle movement states

Claims (10)

1. Method for selecting a mode of operation of a hybrid vehicle, in which a mode of operation, ideal for the current instant of operation in respect of predetermined target criteria, with a corresponding ideal power distribution in the drivetrain is selected from a predetermined set of possible modes of operation (1a) of a hybridized drivetrain, wherein at least one evaluation variable is determined (S1) for each of the predetermined target criteria for the quantitative description of the respective target criterion, comprising the following steps:

   - determining possible power distributions (2) in the drivetrain (S3) for each mode of operation of a first selection (1c) of possible modes of operation; and

   - determining values (5, 6, 7) of the evaluation variables for each one of the determined power distributions (2) of the respective mode of operation for each mode of operation of the first selection and determining an ideal power distribution (2a) in the drivetrain by means of the determined values of the evaluation variables for the respective mode of operation (S4); and

   - selecting an ideal mode of operation by means of those values of the evaluation variables which the latter have for the modes of operation of the first selection, in each case at the operating point of the determined ideal power distribution (S5) characterized in that the following steps are carried out for each mode of operation of the first selection for the purposes of determining the ideal power distribution for each mode of operation of the first selection (S4):

   a) determining a first ideal value (5a, 6a, 7a) of the evaluation variable for each evaluation variable in the respective mode of operation, wherein the first ideal value is preferably (S42)

   a1) set as an extremal value (5a, 7a) of the determined values of the evaluation variable in the respective mode of operation if the target described by the evaluation variable is an extremization target, and

   a2) predetermined as a setpoint value (6a) which is independent of the determined values of the evaluation variable in the respective mode of operation if the target described by the evaluation variable is an approximation target;

   b) determining weighted first deviations (5c, 6c, 7c) of the values of the respective evaluation variable for each evaluation variable from the determined first ideal value (5a, 6a, 7a) of the respective evaluation variable (S43); and

   c) determining the ideal power distribution of the respective mode of operation using a predetermined first decision rule (S44), which sets (S44) an ideal power distribution (2a) depending on the determined weighted first deviations (5c, 6c, 7c) of the evaluation variables; and/or in that the following steps are carried out for selecting an ideal mode of operation from the first selection of possible modes of operation:

   a) determining a second ideal value (5d, 6d, 7d), valid for all modes of operation of the first selection, of the evaluation variable (S52) for each evaluation variable, wherein the second ideal value is preferably

   a1) set as an extremal value of the values (5b, 7b) of the respective evaluation variables at the operating point of the determined ideal power distribution in all modes of operation of the first selection if the target described by the evaluation variable is an extremization target, and

   a2) predetermined as a setpoint value (6d) which is independent of the determined values (5b, 7b) of the respective evaluation variable if the target described by the evaluation variable is an approximation target;

   b) determining a weighted second deviation (5e, 6e, 7e), which is respectively determined for each evaluation variable in each mode of operation of the first selection and which respectively specifies (S53) a weighted deviation of the second ideal value (5b, 6d, 7d) of the respective evaluation variable from the value of the evaluation variables at the operating point (2a) of the determined ideal power distribution for the respective mode of operation,

   c) selecting the ideal mode of operation using a predetermined second decision rule which evaluates the modes of operation of the first selection, in each case in a manner dependent on the weighted second deviations (5e, 6e, 7e) of the evaluation variables, and sets (S54) an ideal mode of operation (BA2).
2. Method according to Claim 1, characterized by at least one weighting factor for each evaluation variable for determining the weighted

   a) first deviations (5c, 6c, 7c), which weighting factor scales the difference in terms of magnitude of the values of the evaluation variables from the first ideal value of the respective evaluation variable to a dimensionless basis or to a cost basis common to all evaluation variables; and/or

   b) second deviations (5e, 6e, 7e, 8e), which weighting factor scales the difference in terms of magnitude of the values of the evaluation variables from the second ideal value of the respective evaluation variable to a dimensionless basis or to a cost basis common to all evaluation variables.
3. Method according to Claim 1 or 2, characterized

   a) in that, in accordance with the second decision rule, the weighted second deviations (5e, 6e, 7e, 8e) of the evaluation variables of in each case one mode of operation are summed and the mode of operation whose summed value (S2) of the summed second deviations (□1, □2, □3) is minimal is set as an ideal mode of operation; and/or

   b1) in that the first decision rule is a minimax decision rule and/or

   b2) in that, in accordance with the first decision rule, the largest first deviation (10) is established at each operating point of the possible power distributions of one mode of operation, in each case from the values of the weighted first deviations (5c, 6c, 7c) of the evaluation variables, and the operating point (2a) which has the smallest (10a) one of the established largest first deviations (10) is set as ideal power distribution.
4. Method according to one of the preceding claims, characterized in that determining the first selection of the modes of operation comprises the following steps:

   a) predetermining possible vehicle movement states (11) of the hybrid vehicle, possible modes of operation of the drivetrain and an assignment (12) specifying which ones of the possible modes of operation are admissible in which vehicle movement state;

   b) determining a current vehicle movement state; and

   c) restricting the modes of operation of the vehicle to those that are assigned to the determined current vehicle movement state (S21).
5. Method according to Claim 4, characterized in that determining the first selection of the modes of operation comprises the following steps:

   a) predetermining at least one restriction rule which sets which modes of operation are currently available depending on at least one operating parameter and independently of a current torque requirement and the current vehicle movement state; and

   b) restricting the modes of operation of the vehicle to those which are currently available in accordance with the at least one restriction rule (S22) .
6. Method according to Claim 5, characterized in that, in accordance with the at least one restriction rule, an availability of components in the drivetrain is checked, in particular of an electric energy store and/or an electric traction machine.
7. Method according to one of the preceding claims, characterized

   a) in that values (9) of the power distribution of the modes of operation which should be excluded as possible operating points are determined on the basis of predetermined exclusion criteria, and

   b) in that, for the purposes of determining the ideal power distribution for each mode of operation of the first selection, only those values of the possible power distribution and the respectively assigned values of the evaluation variables which were not excluded by means of the exclusion criteria are used (S45).
8. Method according to one of the preceding claims, characterized

   a) in that the vehicle is a parallel hybrid vehicle; and

   b) in that the power distributions in the drivetrain which are possible for each mode of operation are established as the torques generable by the electric traction machine, taking into account the capabilities of the components in the drivetrain; and

   c) in that the ideal power distribution is determined by a torque (2a) of the electric traction machine which is ideal in respect of the predetermined target criteria.
9. Method according to one of the preceding claims, characterized in that the possible modes of operation of the drivetrain comprise at least some of the following modes of operation: pure combustion-engine-related travel, pure electric travel with the combustion engine switched off, electric travel with the combustion engine operating at idle speed, boost operation, brake recuperation, electric start, load-point increase, load-point reduction and the so-called genset operation.
10. Hybrid vehicle, in particular commercial vehicle, comprising a control apparatus for selecting a mode of operation of the hybrid vehicle configured to carry out the method according to one of Claims 1 to 9.

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